Optimizing Choke Size to Minimize Sand Production in Oil Wells: A Machine Learning Approach

Year 2025, Volume: 12 Issue: 2, 541 - 561, 30.06.2025

Sunday Agbons Igbinere , İkponmwosa Ohenhen , Edobor Frankie Christopher

https://doi.org/10.54287/gujsa.1669814

Abstract

This study investigates the relationship between operational parameters like choke size etc. and sand production in some oilfields, aiming to optimize efficiency while minimizing sand-related challenges. Data visualization revealed trends in sand cut behaviour under varying gross rate, net rate, and BS&W conditions. Three machine learning models artificial neural network (ANN), Random Forest (RF) and extreme gradient boosting (XGBoost) were developed, with XGBoost achieving the highest accuracy. Extreme gradient boosting (XGBoost) outperformed the others by achieving the highest R-squared value of 0.952 and the lowest mean absolute error (MAE) and mean squared error (MSE), demonstrating its superior accuracy in predicting sand cut values. Shapley additive exPlanations (SHAP) analysis highlighted key parameters like manifold pressure, gas rate, and BS&W in predicting sand cut. Optimization using the Mealpy Genetic Algorithm yielded an optimal configuration gross rate of 750blpd, sand cut of 0.25pptb, and BS&W of 5.5%. Sensitivity analysis emphasized monitoring separator pressure and gas rate. The findings demonstrate the potential of integrating machine learning and optimization to enhance decision-making, reduce risks, and improve production efficiency. Recommendations for implementation and future research are provided to ensure sustainable operations.

Keywords

Sand Production , Machine Learning , SHAP , ANN , XGBoost , Random Forest , Optimization , Choke , Genetic Algorithm , Sand Cut

Ethical Statement

Academic Standards and Excellence

Supporting Institution

University of Benin

Thanks

A special appreciation to Dergipark and Gazi University

References

• Abdelghany, W. K., Hammed, M. S., Radwan, A. E., & Nassar, T. (2022). Implications of machine learning on geomechanical characterization and sand management: A case study from Hilal Field, Gulf of Suez, Egypt. Journal of Petroleum Exploration and Production Technology. https://doi.org/10.1007/s13202-022-01551-9
• Ali, M., Prasad, R., Xiang, Y. & Yaseen, Z. M. (2020). Complete Ensemble Empirical Mode Decomposition Hybridized with Random Forest and Kernel Ridge Regression Model for Monthly Rainfall Forecasts, J. Hydrol. 584(2020) 124647, https://doi.org/10.1016/J.JHYDROL.2020.124647
• Al-Shaaibi, S. K., Al-Ajmi, A. M., & Al-Wahaibi, Y. (2013). Three dimensional modeling for predicting sand production. Journal of Petroleum Science and Engineering, 109, 348–363.
• Alshboul, O., Shehadeh, A., Almasabha, G., & Almuflih, A. S. (2022). Extreme Gradient Boosting-Based Machine Learning Approach for Green Building Cost Prediction. Sustainability, 14(11), 6651. https://doi.org/10.3390/su14116651
• Ani, M., Oluyemi, G., Petrovski, A., & Rezaei-Gomari, S. (2016). Reservoir Uncertainty Analysis: the Trends from Probability to Algorithms and Machine Learning. SPE Intelligent Energy International Conference and Exhibition, 6-8 September 2016, Aberdeen, UK.
• Anifowose, F. A., Labadin, J. & Abdulraheem, A. (2017a). Hybrid Intelligent Systems in Petroleum Reservoir Characterization and Modeling: The Journey so Far and the Challenges Ahead. Journal of Petroleum Exploration and production Technology, 7(1) (2017), 251-263.
• Anifowose, F. A., Labadin, J. & Abdulraheem, A. (2017b). Ensemble Machine Learning: An Untapped Modeling Paradigm for Petroleum Reservoir Characterization. J. Petrol. Sci. Eng., 151(2017), 480-487.
• Anirbid, S., Kriti, Y., Kamakshi, R., Namrata, B. & Hemangi, O. (2021). Application of Machine Learning and Artificial Intelligence in Oil and Gas Industry. Petroleum Research, 6(4), 379-391. https://doi.org/10.1016/j.ptlrs.2021.05.009
• Azad M., Zargar G. & Arabjamaloei, R. (2011). A New Approach to Sand Production Onset Prediction Using Artificial Neural Networks. Petroleum Science and Technology, 29:19, 1975-1983. https://doi.org/10.1080/10916460903551081
• Carlson, J., Gurley, D., King, G., Price-Smith, C., & Waters, F. (1992). Sand Control: Why and How. Oilfield Review; Netherlands, 4(4).
• Completion Technology for Unconsolidated Formations, Revision 2, (1995)
• Completion Technology for Unconsolidated Formations, Revision 3, (1997). https://fr.scribed.com
• Dickson, R. L., Raymond, C. A., Joe, W., & Wilkinson, C. A. (2003). Segregation of Eucalyptus Dunnii Logs using Acoustics. Forest Ecology and Management, 179(1-3), 243-251.
• Fattahpour, V., Moosavi, M., & Mehranpour, M. (2012). An experimental investigation on the effect of rock strength and perforation size on sand production. Journal of Petroleum Science and Engineering, 86–87, 172–189.
• Fuh, G., & Morita, N. (2013). Sand production prediction analysis of heterogeneous reservoirs for sand control and optimal well completion design. International Petroleum Technology Conference. https://doi.org/10.2523/16940-MS
• Gharagheizi, F., Mohammadi, A., Arabloo, M., & Shokrollahi, A. (2017). Prediction of sand production onset in petroleum reservoirs using a reliable classification approach. Petroleum, 3(2), 280–285. https://doi.org/10.1016/j.petlm.2016.02.001
• Han, G., Shepstone, K., Harmawan, I., Er, U., Jusoh, H., Lin, L. S., Pringle, D., et al. (2011). A comprehensive study of sanding rate from a gas field, from reservoir to completion, production, and surface facilities. SPE Journal, 16(2), 463–481.
• van den Hoek, P. J., Hertogh, G. M. M., Kooijman, A. P., de Bree, Ph., Kenter, C. J., & Papamichos, E. (2007). A new concept of sand production prediction, theory and laboratory experiments. SPE Drilling & Completion, 15(4), 261–273.
• Kanj, M. Y. & Abousleiman, Y. (1999). Realistic Sanding Predictions: A Neural Approach. Paper Presented at the SPE Annual Technical Conference and Exhibition, October 3–6, 1999. SPE-56631-MS. https://doi.org/10.2118/56631-MS
• Kessler, N., Wang, Y., & Santarelli, F. J. (1993). A simplified pseudo 3D model to evaluate sand production risk in deviated cased holes. SPE Annual Technical Conference and Exhibition.
• Ketmalee, T., & Bandyopadhyay, P. (2018). Application of neural network in formation failure model to predict sand production. In Offshore Technology Conference Asia 2018 (pp. 1–10). https://doi.org/10.4043/28506-ms
• Khamehchi, E. Kivi, I. R. & Akbari, M. (2014). A Novel Approach to Sand Production Prediction using Artificial Intelligence. Journal of Petroleum Science and Engineering. 123(2014), 147-154. https://doi.org/10.1016/j.petrol.2014.07.033
• Kozhagulova, A., Shabdirova, A., Minh, N. H., & Zhao, Y. (2021). An integrated laboratory experiment of realistic diagenesis, perforation and sand production using a large artificial sandstone specimen. Journal of Rock Mechanics and Geotechnical Engineering.
• Morita, N., Whitfill, D. L., Fedde, O. P., & Lovik, T. H. (1989). Parametric study of sand-production prediction: Analytical approach. SPE Production Engineering, 4(1), 25–33. https://doi.org/10.2118/16990-PA
• Ngwashi, A. R., Ogbe, D. O., & Udebhulu, D. O. (2021). Evaluation of machine-learning tools for predicting sand production. In SPE Nigeria Annual International Conference and Exhibition 2021, NAIC 2021, 1–16. https://doi.org/10.2118/207193-MS
• Nosakhare, A., Igemhokhai, S., Aimhanesi, S., Ugbodu, F. & Iyore, N. (2024). Heliyon Data-Driven Intelligent Modeling, Optimization, and global sensitivity analysis of a xanthan gum biosynthesis process. Heliyon, 10(3), e25432. https://doi.org/10.1016/j.heliyon.2024.e25432
• Nouri, A., Vaziri, H., Belhaj, H., & Islam, R. (2006). Sand-production prediction, a new set of criteria for modeling based on large-scale transient experiments and numerical investigation. SPE Journal, 11(2), 26–29.
• Papamichos, E., & Furui, K. (2019). Analytical models for sand onset under field conditions. Journal of Petroleum Science and Engineering, 172, 171–189.
• Shabdirova, A., Minh, N. H., & Zhao, Y. (2022). Role of plastic zone porosity and permeability in sand production in weak sandstone reservoirs. Underground Space (China). https://doi.org/10.1016/j.undsp.2021.10.005
• Shabdirova, A., Kozhagulova, A., Samenov, Y., & others. (2024). Sand production prediction with machine learning using input variables from geological and operational conditions in the Karazhanbas oilfield, Kazakhstan. Natural Resources Research, 33, 2789–2805. https://doi.org/10.1007/s11053-024-10389-3
• Sheridan, R. P., Wang, W. M., Liaw, A., Mu, J. & Gifford, E. M. (2016). Extreme Gradient Boosting as a Method for Quantitative Structure-Activity Relationship. Journal of Chemical Information and Modeling, 56(12), 2353-2360. https://doi.org/10.1021/acs.jcim.6b00591
• Skjaerstein, A., Tronvoll, J., Santarelli, F. J., & Joranson, H. (1997). Effect of water breakthrough on sand production, experimental and field evidence. In Proceedings - SPE Annual Technical Conference and Exhibition Pi, 565–575.
• Song, J., Li, Y., Liu, S., Xiong, Y., Pang, W., He, Y. & Mu, Y. (2022). Comparison of Machine Learning Algorithms for Sand Production Prediction: An Example for a Gas-Hydrate-Bearing Sand Case. Energies 2022, 15(18), 6509. https://doi.org/10.3390/en15186509
• Speiser, J. L., Miller, M. E., Tooze, J. & Ip, E. (2019). A Comparison of Random Forest Variable Selection Methods for Classification Prediction Modeling. Expert Systems with Applications, 134, 93–101. https://doi.org/10.1016/j.eswa.2019.05.028
• Tiab, D. & Donaldson, E. C. (2004). Petrophysics: Theory and Practice of Measuring Reservoir Rock and Fluid Transport Properties, Gulf Professional Publishing, Elsevier (2nd. Ed., pp. 554-670)
• Tixier, M.P. (1949) Evaluation of Permeability from Log Resistivity Gradients. Oil and Gas Journal, 48, 113-122.
• Wang, Y., & Dusseault, M. B. (2010). Sand production potential near inclined perforated wellbores. In 47th Annual Technical Meeting of the Petroleum Society. https://doi.org/10.2118/9670
• Weingarten, J. S., & Perkins, T. K. (2007). Prediction of sand production in gas wells, methods and Gulf of Mexico case studies. Journal of Petroleum Technology, 47(7), 596–600.
• Wu, B., Choi, S. K., Denke, R., Barton, T., Viswanathan, C., Lim, S., Zamberi, M., & Shaffee, S. (2016). A new and practical model for amount and rate of sand production. Offshore Technology Conference.